- Online
- $1,050
- Requires a prerequisite course
This course can be applied toward the UBC Certificate in Key Capabilities in Data Science. You must complete Programming in Python for Data Science before starting this course.
This course focuses on practical tech skills, with emphasis on transferable knowledge and a critical thinking approach for immediate application in your current work.
Explore machine learning for prediction by focusing on regression and classification models. Understand how to map data to the correct model type, evaluate and select models, and communicate and interpret model results to help organizations reduce operating costs, optimize market strategies and identify trends.
By the end of this course, you’ll be able to:
- Describe supervised learning and identify what kind of tasks it is suitable for
- Explain common machine learning concepts such as classification and regression, training and testing, overfitting, parameters and hyperparameters and the golden rule
- Choose a correct predictive modelling technique (e.g., regression or classification) given the available data
- Identify when and why to apply data pre-processing techniques such as scaling and one-hot encoding
- Describe at a high level how common machine learning algorithms work, including decision trees, k-nearest neighbours and linear regression
- Use Python and the scikit-learn package to develop an end-to-end supervised machine learning pipeline
Basic knowledge of Python and working with data is required. This course uses Python for data science and the scikit-learn package.
